Linearisation techniques and the dual algorithm for a class of mixed singular/continuous control problems in reinsurance. Part II: Numerical aspects
This paper is intended as a companion to [12] and focuses on the numerical implementations of the method in the context of insurance problems with mixed controls: reinsurance acting as continuous control, and capital injection and dividend payment acting as singular ones. The aim is twofold. On the...
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| Published in: | Applied mathematics and computation Vol. 473; p. 128655 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier Inc
15.07.2024
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| Subjects: | |
| ISSN: | 0096-3003, 1873-5649 |
| Online Access: | Get full text |
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| Summary: | This paper is intended as a companion to [12] and focuses on the numerical implementations of the method in the context of insurance problems with mixed controls: reinsurance acting as continuous control, and capital injection and dividend payment acting as singular ones. The aim is twofold. On the one hand, we provide a comparison of the output of our method in terms of the optimal value against two benchmarks. The first benchmark is provided by [4] in the no-reinsurance setting and when the claims are exponentially distributed. The second benchmark refers to [8] where capital injections are not permitted, but different claim distributions can be employed. On the other hand, we illustrate the improvement of the models in [8] when capital injection is allowed. We also look into the effect of reinsurance in the model [4]. Several cases for the equity cost parameter are discussed as are the boundary (or limit) situations.
•We provide a validation of the Dual Linear Programming algorithm by showcasing its performance on two benchmarks.•With a relatively modest order of moments (4), the relative errors are of at most 3%.•Leveraging this algorithm reveals significant enhancements in the expected dividends, advocating for capital injections. |
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| ISSN: | 0096-3003 1873-5649 |
| DOI: | 10.1016/j.amc.2024.128655 |